Would you actually override the Serasa score or not?

It aligned with the product and growth strategy to incentivize and create virality within the product. In the same year, we also started looking at negative values of people with negative debt in the bureaus. Back then in Brazil, credit reports only had information about who consulted your information, like your CPF (Social Security number in Brazil), and whether you defaulted on a credit product. There wasn't a positive bureau, only a negative bureau.

Would you actually override the Serasa score or not?

We could collect data on whether you hadn't paid a telco bill, credit card, personal loan, mortgage, etc. We decided to apply the classic rule. If you have a negative mark, you have not paid anything or you're currently delinquent, we would block you from acceptance. That helped on reducing delinquency. Our policy for the first year was if you're doing 700, let's do 650, and see how it performs, and then we would wait a few months to see its performance.

Would you actually override the Serasa score or not?

The first model was launched in March 2016, primarily based on bureau information and KYC data shared by customers during the application process. This data was collected through the net process, combined with bureau information like credit reports and scores. Naturally, this model was much better than what we had before. However, since we applied features like high scores or the absence of negative marks, we had to maintain those filters. Machine learning models are excellent at interpolation but struggle with extrapolation. Removing those filters would mean the model hadn't encountered such customers before, making it difficult to accurately predict default rates for customers not part of the previously approved population. That was our situation in 2016.